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Qian S, Wang Z, Chao H, Xu Y, Wei Y, Gu G, Zhao X, Lu Z, Zhao J, Ren J, Jin S, Li L, Chen K. Application of adaptive chaotic dung beetle optimization algorithm to near-infrared spectral model transfer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124718. [PMID: 38950481 DOI: 10.1016/j.saa.2024.124718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/08/2024] [Accepted: 06/24/2024] [Indexed: 07/03/2024]
Abstract
A new transfer approach was proposed to share calibration models of the hexamethylenetetramine-acetic acid solution for studying hexamethylenetetramine concentration values across different near-infrared (NIR) spectrometers. This approach combines Savitzky-Golay first derivative (S_G_1) and orthogonal signal correction (OSC) preprocessing, along with feature variable optimization using an adaptive chaotic dung beetle optimization (ACDBO) algorithm. The ACDBO algorithm employs tent chaotic mapping and a nonlinear decreasing strategy, enhancing the balance between global and local search capabilities and increasing population diversity to address limitations observed in traditional dung beetle optimization (DBO). Validated using the CEC-2017 benchmark functions, the ACDBO algorithm demonstrated superior convergence speed, accuracy, and stability. In the context of a partial least squares (PLS) regression model for transferring hexamethylenetetramine-acetic acid solutions using NIR spectroscopy, the ACDBO algorithm excelled over alternative methods such as uninformative variable elimination, competitive adaptive reweighted sampling, cuckoo search, grey wolf optimizer, differential evolution, and DBO in efficiency, accuracy of feature variable selection, and enhancement of model predictive performance. The algorithm attained outstanding metrics, including a determination coefficient for the calibration set (Rc2) of 0.99999, a root mean square error for the calibration set (RMSEC) of 0.00195%, a determination coefficient for the validation set (Rv2) of 0.99643, a root mean squared error for the validation set (RMSEV) of 0.03818%, residual predictive deviation (RPD) of 16.72574. Compared to existing OSC, slope and bias correction (S/B), direct standardization (DS), and piecewise direct standardization (PDS) model transfer methods, the novel strategy enhances the accuracy and robustness of model predictions. It eliminates irrelevant background information about the hexamethylenetetramine concentration, thereby minimizing the spectral discrepancies across different instruments. As a result, this approach yields a determination coefficient for the prediction set (Rp2) of 0.96228, a root mean squared error for the prediction set (RMSEP) of 0.12462%, and a relative error rate (RER) of 17.62331, respectively. These figures closely follow those obtained using DS and PDS, which recorded Rp2, RMSEP, and RER values of 0.97505, 0.10135%, 21.67030, and 0.98311, 0.08339%, 26.33552, respectively. Unlike conventional methods such as OSC, S/B, DS, and PDS, this novel approach does not require the analysis of identical samples across different instruments. This characteristic significantly broadens its applicability for model transfer, which is particularly beneficial for transferring specific measurement samples.
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Affiliation(s)
- Shichuan Qian
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zhi Wang
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Hui Chao
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Yinguang Xu
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Yulin Wei
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Guanghui Gu
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Xinping Zhao
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Zhiyan Lu
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Jingru Zhao
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Jianmei Ren
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Shaohua Jin
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Lijie Li
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Kun Chen
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
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2
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Anuschek M, Skelbæk-Pedersen AL, Skibsted E, Kvistgaard Vilhelmsen T, Axel Zeitler J, Rantanen J. THz-TDS as a PAT tool for monitoring blend homogeneity in pharmaceutical manufacturing of solid oral dosage forms: A proof-of-concept study. Int J Pharm 2024; 662:124534. [PMID: 39079591 DOI: 10.1016/j.ijpharm.2024.124534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/26/2024] [Accepted: 07/26/2024] [Indexed: 08/05/2024]
Abstract
The process analytical technology (PAT) framework is well established and integral to facilitate process understanding, enable a transition from batch to continuous manufacturing, and improve product quality. Near-infrared (NIR) spectroscopy has been established as a standard PAT tool for many process analytical challenges, including monitoring powder blend homogeneity. However, alternative technologies for monitoring powder blending are of interest due to the importance of the blending step in manufacturing solid oral dosage forms. Terahertz time-domain spectroscopy (THz-TDS) is therefore explored in this study as an alternative tool for monitoring blend homogeneity with the potential for endpoint control in a batch blending process. Powder blends of microcrystalline cellulose (MCC) and dibasic calcium phosphate dihydrate and blends of MCC and granulated α-lactose monohydrate were investigated non-invasively at various compositions using THz-TDS in transmission mode for acquiring spectra from samples enclosed in the blending container. It was found that attenuation- and phase-related parameters acquired with THz-TDS could reliably resolve physical changes related to the homogeneity of the blend. Further evaluations revealed that changes in the bulk density of the blend, in addition to the intrinsic optical properties of the materials, played a critical role in the observed trends for both systems. In contrast, the scattering contribution of the powder was mainly crucial for the attenuation-related parameter in blends with materials of high refractive indices. Finally, THz-TDS measurements were acquired throughout a blending process mimicking a continuous acquisition. The method could follow blending dynamics and resulted in reasonable predictive errors of the content of 0.5 - 2.5 %. Relative standard deviations for high content blends (20 %) were acceptable (3 - 7 %) whereas at low contents (5 %) significantly higher values (9 - 35 %) were found. Based on these findings, THz-TDS is a feasible PAT tool for monitoring blend homogeneity and controlling high content blend processes, although precision and accuracy is considered to improve with a more suitable interface.
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Affiliation(s)
- Moritz Anuschek
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk A/S, ET Oral Product Development, Måløv, Denmark.
| | | | - Erik Skibsted
- Novo Nordisk A/S, ET Oral Product Development, Måløv, Denmark
| | | | - J Axel Zeitler
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Jukka Rantanen
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
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Tanzilli D, Strani L, Bonacini F, Ferrando A, Cocchi M, Durante C. Implementing multiblock techniques in a full-scale plant scenario: On-line prediction of quality parameters in a continuous process for different acrylonitrile butadiene styrene (ABS) products. Anal Chim Acta 2024; 1316:342851. [PMID: 38969408 DOI: 10.1016/j.aca.2024.342851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/05/2024] [Accepted: 06/07/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND The study explores the challenges of handling multiblock data of different natures (process and NIR sensors) for on-line quality prediction in a full-scale plant scenario, namely a plant operating in continuous on an industrial scale and producing different grade Acrylonitrile Butadiene Styrene (ABS) products. This environment is an ideal scenario to evaluate the use of multiblock data analysis methods, which can enhance data interpretation, visualization, and predictive performances. In particular, a novel multiblock extension of Locally Weighted PLS has been proposed by the authors, namely Locally Weighted Multiblock Partial Least Squares (LW-MB-PLS). Response-Oriented Sequential Alternation (ROSA) has also been employed to evaluate the diverse block relevance for the prediction of two quality parameters associated with the polymer. Data are split in blocks both according to sensor type and different plant sections, and different models have been built by incremental addition of data blocks to evaluate if early estimation of product quality is feasible. RESULTS ROSA method showed promising predictive performance for both quality parameters, highlighting the most influential plant sections through the selection of data blocks. The results suggested that both early and late-stage sensors play crucial roles in predicting product quality. A reasonable estimation of quality parameters before production completion has been achieved. On the other hand, the proposed LW-MB-PLS, while comparable in predictive performances, allowed reducing systematic prediction errors for specific products. SIGNIFICANCE This study contributes valuable insights for continuous production processes, aiding plant operators and paving the way for advancements in online quality prediction and control. Furthermore, it is implemented as a locally weighted extension of MB-PLS.
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Affiliation(s)
- Daniele Tanzilli
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via 4 Campi 103, 41125, Modena, Italy; Centre National de la Recherche Scientifique (CNRS), Laboratoire de Spectroscopie pour les Interactions, la Réactivitè et l'Environnement (LASIRE), Cité Scientifique, University Lille, F-59000, Lille, France
| | - Lorenzo Strani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via 4 Campi 103, 41125, Modena, Italy.
| | - Francesco Bonacini
- Research Center, Versalis (ENI) S.p.A., Via Taliercio 14, 46100, Mantova, Italy
| | - Angelo Ferrando
- Research Center, Versalis (ENI) S.p.A., Via Taliercio 14, 46100, Mantova, Italy
| | - Marina Cocchi
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via 4 Campi 103, 41125, Modena, Italy
| | - Caterina Durante
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via 4 Campi 103, 41125, Modena, Italy
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Mészáros LA, Gyürkés M, Varga E, Tacsi K, Honti B, Borbás E, Farkas A, Nagy ZK, Nagy B. Real-time release testing of in vitro dissolution and blend uniformity in a continuous powder blending process by NIR spectroscopy and machine vision. Eur J Pharm Biopharm 2024; 201:114368. [PMID: 38880401 DOI: 10.1016/j.ejpb.2024.114368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/22/2024] [Accepted: 06/13/2024] [Indexed: 06/18/2024]
Abstract
Continuous manufacturing is gaining increasing interest in the pharmaceutical industry, also requiring real-time and non-destructive quality monitoring. Multiple studies have already addressed the possibility of surrogate in vitro dissolution testing, but the utilization has rarely been demonstrated in real-time. Therefore, in this work, the in-line applicability of an artificial intelligence-based dissolution surrogate model is developed the first time. NIR spectroscopy-based partial least squares regression and artificial neural networks were developed and tested in-line and at-line to assess the blend uniformity and dissolution of encapsulated acetylsalicylic acid (ASA) - microcrystalline cellulose (MCC) powder blends in a continuous blending process. The studied blend is related to a previously published end-to-end manufacturing line, where the varying size of the ASA crystals obtained from a continuous crystallization significantly affected the dissolution of the final product. The in-line monitoring was suitable for detecting the variations in the ASA content and dissolution caused by the feeding of ASA with different particle sizes, and the at-line predictions agreed well with the measured validation dissolution curves (f2 = 80.5). The results were further validated using machine vision-based particle size analysis. Consequently, this work could contribute to the advancement of RTRT in continuous end-to-end processes.
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Affiliation(s)
- Lilla Alexandra Mészáros
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Martin Gyürkés
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Emese Varga
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Kornélia Tacsi
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Barbara Honti
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Enikő Borbás
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
| | - Brigitta Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
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Velez-Silva NL, Drennen JK, Anderson CA. Continuous manufacturing of pharmaceutical products: A density-insensitive near infrared method for the in-line monitoring of continuous powder streams. Int J Pharm 2024; 650:123699. [PMID: 38081558 DOI: 10.1016/j.ijpharm.2023.123699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/05/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023]
Abstract
Near infrared (NIR) spectroscopy is a valuable analytical technique for monitoring chemical composition of powder blends in continuous pharmaceutical processes. However, the variation in density captured by NIR during spectral collection of dynamic powder streams at different flow rates often reduces the performance and robustness of NIR models. To overcome this challenge, quantitative NIR measurements are commonly collected across all potential manufacturing conditions, including multiple flow rates to account for the physical variations. The utility of this approach is limited by the considerable quantity of resources required to run and analyze an extensive calibration design at variable flow rates in a continuous manufacturing (CM) process. It is hypothesized that the primary variation introduced to NIR spectra from changing flow rates is a change in the density of the powder from which NIR spectra are collected. In this work, powder stream density was used as an efficient surrogate for flow rate in developing a quantitative NIR method with enhanced robustness against process rate variation. A density design space of two process parameters was generated to determine the conditions required to encompass the apparent density and spectral variance from increases in process rate. This apparent density variance was included in calibration at a constant low flow rate to enable the development of a density-insensitive NIR quantitative model with limited consumption of materials. The density-insensitive NIR model demonstrated comparable prediction performance and flow rate robustness to a traditional NIR model including flow rate variation ("gold standard" model) when applied to monitoring drug content in continuous runs at varying flow rates. The proposed platform for the development of in-line density-insensitive NIR methods is expected to facilitate robust analytical model performance across variable continuous manufacturing production scales while improving the material efficiency over traditional robust modeling approaches for calibration development.
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Affiliation(s)
- Natasha L Velez-Silva
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, United States; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, United States
| | - James K Drennen
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, United States; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, United States
| | - Carl A Anderson
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, United States; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, United States.
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Movilla-Meza NA, Sierra-Vega NO, Alvarado-Hernández BB, Méndez R, Romañach RJ. The Use of a Closed Feed Frame for the Development of Near-Infrared Spectroscopic Calibration Model to Determine Drug Concentration. Pharm Res 2023; 40:2903-2916. [PMID: 37700106 DOI: 10.1007/s11095-023-03601-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023]
Abstract
PURPOSE This study evaluates the use of the closed feed frame as a material sparing approach to develop near-infrared (NIR) spectroscopic calibration models for monitoring blend uniformity. The effect of shear induced by recirculation on NIR spectra was also studied. METHODS Calibration models were developed using NIR spectra obtained in the closed feed frame for two cases. For case 2, blends that flowed through the open feed frame were predicted with the model. The shear effect of the feed frame on the blends was assessed through the characterization of powder properties before and after recirculation. RESULTS The physical characterization of the blends confirmed that the powder properties were not altered after recirculation within the closed feed frame. Both calibration models provided highly accurate predictions of the test sets with low bias (0.03% w/w and -0.06% w/w) and relative standard error of prediction (1.9% and 3.7%), respectively. The predictive performance of the calibration models was not affected by the shear effect. CONCLUSION Recirculation within the closed feed frame did not change the physical properties of the blends studied. The prediction of blends flowing through the open feed frame was possible with a calibration model developed in the closed feed frame. The closed feed frame could reduce the materials needed to develop calibration models by more than 90%.
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Affiliation(s)
| | - Nobel O Sierra-Vega
- Department of Chemical Engineering, University of Puerto Rico at Mayagüez, Mayagüez, PR, USA
| | | | - Rafael Méndez
- Department of Chemical Engineering, University of Puerto Rico at Mayagüez, Mayagüez, PR, USA
| | - Rodolfo J Romañach
- Department of Chemistry, University of Puerto Rico at Mayagüez, Mayagüez, PR, USA.
- Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemistry, University of Puerto Rico at Mayagüez, PO Box 9000, Mayagüez, PR, 00681, USA.
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Velez-Silva NL, Drennen JK, Anderson CA. Influence of powder stream density on near infrared measurements upon scale-up of a simulated continuous process. Int J Pharm 2023; 645:123354. [PMID: 37647977 DOI: 10.1016/j.ijpharm.2023.123354] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/17/2023] [Accepted: 08/26/2023] [Indexed: 09/01/2023]
Abstract
Near-infrared (NIR) spectroscopy is a powerful process analytical tool for monitoring chemical constituents in continuous pharmaceutical processes. However, the density variation introduced when quantitative NIR measurements are performed on powder streams at different flow rates is a potential source of a lack of model robustness. Since different flow rates are often required to meet the production requirements (e.g., during scale-up) of a continuous process, the development of efficient strategies to characterize, understand, and mitigate the impact of powder density on NIR measurements is highly desirable. This study focused on assessing the effect of powder physical variation on NIR by enabling the in-line characterization of powder stream density in a simulated continuous system. The in-line measurements of powder stream density were facilitated through a unique analytical interface to a flowing process. Powder streams delivered at various design levels of flow rate and tube angle were monitored simultaneously by NIR diffuse reflectance spectroscopy, live imaging, and dynamic mass characterization. Statistical analysis and multivariate modeling confirmed powder density as a significant source of spectral variability due to flow rate. Besides providing broader process understanding, results elucidated potential mitigation strategies to facilitate effective continuous process scale-up while ensuring NIR model robustness against density.
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Affiliation(s)
- Natasha L Velez-Silva
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, United States; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, United States
| | - James K Drennen
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, United States; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, United States
| | - Carl A Anderson
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, United States; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, United States.
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8
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Rish AJ, Henson SR, Velez-Silva NL, Nahid Hasan M, Drennen JK, Anderson CA. Application of a wavelength angle mapper for variable selection in iterative optimization technology predictions of drug content in pharmaceutical powder mixtures. Int J Pharm 2023; 643:123261. [PMID: 37479099 DOI: 10.1016/j.ijpharm.2023.123261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/11/2023] [Accepted: 07/18/2023] [Indexed: 07/23/2023]
Abstract
Process analytical technology (PAT) is an essential tool within pharmaceutical manufacturing to ensure consistent quality and maintain process control. Near-infrared (NIR) spectroscopy is one of the most popular PAT techniques, particularly for monitoring active pharmaceutical ingredient (API) concentrations. To interpret the spectral outputs of NIR spectroscopy, advanced multivariate models are required. Calibration-free models such as iterative optimization technology (IOT) algorithms are increasingly of interest, due primarily to their reduced material and time burdens. Variable/wavelength selection is a common method to improve prediction performance and robustness for IOT by focusing on spectral regions with the most relevant information. However, currently proposed wavelength selection approaches rely on training sets for optimization, therefore reducing or removing the advantages of IOT over empirical calibration-dependent models. In this work, a true calibration-free wavelength selection method is proposed based on measuring the difference between individual wavelengths of a mixture spectra and the net analyte signals via a wavelength angle mapper (WAM). An extension of the WAM utilizing a spectral window of wavelength instead of individual wavelengths, called SWAM, was also developed. However, the SWAM method does require a small training set to optimize wavelength selection parameters. The WAM and SWAM methods showed similar prediction performance for API in pharmaceutical powder blends when compared against other calibration-dependent models and the base IOT algorithm.
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Affiliation(s)
- Adam J Rish
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA
| | - Samuel R Henson
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA
| | - Natasha L Velez-Silva
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA
| | - Md Nahid Hasan
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA
| | - James K Drennen
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, USA
| | - Carl A Anderson
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, USA.
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9
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Jørgensen AK, Ong JJ, Parhizkar M, Goyanes A, Basit AW. Advancing non-destructive analysis of 3D printed medicines. Trends Pharmacol Sci 2023; 44:379-393. [PMID: 37100732 DOI: 10.1016/j.tips.2023.03.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 03/22/2023] [Accepted: 03/22/2023] [Indexed: 04/28/2023]
Abstract
Pharmaceutical 3D printing (3DP) has attracted significant interest over the past decade for its ability to produce personalised medicines on demand. However, current quality control (QC) requirements for traditional large-scale pharmaceutical manufacturing are irreconcilable with the production offered by 3DP. The US Food and Drug Administration (FDA) and the UK Medicines and Healthcare Products Regulatory Agency (MHRA) have recently published documents supporting the implementation of 3DP for point-of-care (PoC) manufacturing along with regulatory hurdles. The importance of process analytical technology (PAT) and non-destructive analytical tools in translating pharmaceutical 3DP has experienced a surge in recognition. This review seeks to highlight the most recent research on non-destructive pharmaceutical 3DP analysis, while also proposing plausible QC systems that complement the pharmaceutical 3DP workflow. In closing, outstanding challenges in integrating these analytical tools into pharmaceutical 3DP workflows are discussed.
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Affiliation(s)
- Anna Kirstine Jørgensen
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Jun Jie Ong
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Maryam Parhizkar
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Alvaro Goyanes
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain; FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK; FabRx Artificial Intelligence, Carretera de Escairón 14, 27543 Currelos (O Saviñao) Lugo, Spain.
| | - Abdul W Basit
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK; FabRx Artificial Intelligence, Carretera de Escairón 14, 27543 Currelos (O Saviñao) Lugo, Spain.
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10
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Kulkarni VR, Chakka J, Alkadi F, Maniruzzaman M. Veering to a Continuous Platform of Fused Deposition Modeling Based 3D Printing for Pharmaceutical Dosage Forms: Understanding the Effect of Layer Orientation on Formulation Performance. Pharmaceutics 2023; 15:pharmaceutics15051324. [PMID: 37242565 DOI: 10.3390/pharmaceutics15051324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
Three-dimensional (3D) printing of pharmaceuticals has been centered around the idea of personalized patient-based 'on-demand' medication. Fused deposition modeling (FDM)-based 3D printing processes provide the capability to create complex geometrical dosage forms. However, the current FDM-based processes are associated with printing lag time and manual interventions. The current study tried to resolve this issue by utilizing the dynamic z-axis to continuously print drug-loaded printlets. Fenofibrate (FNB) was formulated with hydroxypropyl methylcellulose (HPMC AS LG) into an amorphous solid dispersion using the hot-melt extrusion (HME) process. Thermal and solid-state analyses were used to confirm the amorphous state of the drug in both polymeric filaments and printlets. Printlets with a 25, 50, and 75% infill density were printed using the two printing systems, i.e., continuous, and conventional batch FDM printing methods. Differences between the two methods were observed in the breaking force required to break the printlets, and these differences reduced as the infill density went up. The effect on in vitro release was significant at lower infill densities but reduced at higher infill densities. The results obtained from this study can be used to understand the formulation and process control strategies when switching from conventional FDM to the continuous printing of 3D-printed dosage forms.
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Affiliation(s)
- Vineet R Kulkarni
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Lab, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78705, USA
| | - Jaidev Chakka
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Lab, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78705, USA
| | - Faez Alkadi
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Lab, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78705, USA
| | - Mohammed Maniruzzaman
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Lab, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78705, USA
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Tan A, Wang Y, Zhao Y, Wang B, Li X, Wang AX. Near infrared spectroscopy quantification based on Bi-LSTM and transfer learning for new scenarios. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 283:121759. [PMID: 35985223 DOI: 10.1016/j.saa.2022.121759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 08/04/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
This study proposed a deep transfer learning methodology based on an improved Bi-directional Long Short-Term Memory (Bi-LSTM) network for the first time to address the near infrared spectroscopy (NIR) model transfer issue between samples. We tested its effectiveness on two datasets of manure and polyglutamic acid (γ-PGA) solution, respectively. First, the optimal primary Bi-LSTM networks for cattle manure and the first batch of γ-PGA were developed by ablation experiments and both proved to outperform one-dimensional convolutional neural network (1D-CNN), Partial Least Square (PLS) and Extreme Learning Machine (ELM) models. Then, two types of transfer learning approaches were carried out to determine model transferability to non-homologous samples. For poultry manure and the second batch of γ-PGA, the obtained predicting results verified that the second approach of fine-tuning Bi-LSTM layers and re-training FC layers transcended the first approach of fixing Bi-LSTM layers and only re-training FC layers by reducing the RMSEPtarget of 23.4275% and 50.7343%, respectively. Finally, comparisons with fine-tuning 1D-CNN and other traditional model transfer methods further identified the superiority of the proposed methodology with exceeding accuracy and smaller variation, which decreased RMSEPtarget of poultry manure and the second batch of γ-PGA of 7.2832% and 48.1256%, 67.1117% and 80.6924% when compared to that acquired by fine-tuning 1D-CNN, Tradaboost-ELM and CCA-PLS which were the best of five traditional methods, respectively. The study demonstrates the potential of the Fine-tuning-Bi-LSTM enabled NIR technology to be used as a simple, cost effective and reliable detection tool for a wide range of applications under various new scenarios.
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Affiliation(s)
- Ailing Tan
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
| | - Yunxin Wang
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China.
| | - Yong Zhao
- School of Electrical Engineering, Yanshan University, The Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Qinhuangdao 066004, China
| | - Bolin Wang
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
| | - Xiaohang Li
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
| | - Alan X Wang
- Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76706, USA
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Talwar S, Pawar P, Wu H, Sowrirajan K, Wu S, Igne B, Friedman R, Muzzio FJ, Drennen JK. NIR Spectroscopy as an Online PAT Tool for a Narrow Therapeutic Index Drug: Toward a Platform Approach Across Lab and Pilot Scales for Development of a Powder Blending Monitoring Method and Endpoint Determination. AAPS J 2022; 24:103. [PMID: 36171513 DOI: 10.1208/s12248-022-00748-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 08/31/2022] [Indexed: 01/18/2023] Open
Abstract
An online near-infrared (NIR) spectroscopy platform system for real-time powder blending monitoring and blend endpoint determination was tested for a phenytoin sodium formulation. The study utilized robust experimental design and multiple sensors to investigate multivariate data acquisition, model development, and model scale-up from lab to manufacturing. The impact of the selection of various blend endpoint algorithms on predicted blend endpoint (i.e., mixing time) was explored. Spectral data collected at two process scales using two NIR spectrometers was incorporated in a single (global) calibration model. Unique endpoints were obtained with different algorithms based on standard deviation, average, and distributions of concentration prediction for major components of the formulation. Control over phenytoin sodium's distribution was considered critical due to its narrow therapeutic index nature. It was found that algorithms sensitive to deviation from target concentration offered the simplest interpretation and consistent trends. In contrast, algorithms sensitive to global homogeneity of active and excipients yielded the longest mixing time to achieve blending endpoint. However, they were potentially more sensitive to subtle uniformity variations. Qualitative algorithms using principal component analysis (PCA) of spectral data yielded the prediction of shortest mixing time for blending endpoint. The hybrid approach of combining NIR data from different scales presents several advantages. It enables simplifying the chemometrics model building process and reduces the cost of model building compared to the approach of using data solely from commercial scale. Success of such a hybrid approach depends on the spectroscopic variability captured at different scales and their relative contributions in the final NIR model.
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Affiliation(s)
- Sameer Talwar
- Duquesne University Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA, 15282, USA.,MST-BPDS-Biopharm Product Dev & Supply, GSK, 709 Swedeland Road, King of Prussia, PA, 19406, USA
| | - Pallavi Pawar
- Department of Chemical and Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ, 08854, USA.,Gilead, Foster City, CA, 94404, USA
| | - Huiquan Wu
- Office of Pharmaceutical Quality, CDER, FDA, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
| | - Koushik Sowrirajan
- Office of Pharmaceutical Quality, CDER, FDA, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Suyang Wu
- Office of Pharmaceutical Quality, CDER, FDA, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Benoît Igne
- Duquesne University Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA, 15282, USA
| | - Richard Friedman
- Office of Manufacturing Quality, Office of Compliance, CDER, FDA, Silver Spring, MD, 20993, USA
| | - Fernando J Muzzio
- Department of Chemical and Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ, 08854, USA
| | - James K Drennen
- Duquesne University Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA, 15282, USA.
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